Sea Level Rise and the National Security Challenge of Sustainable Urban Adaptation in Doha and Other Arab Coastal Cities
Bibliographic record
Abstract
The warming of the global ocean and the melting of ice caps have been continuously and increasingly rapidly driving the phenomenon of sea level rise (SLR) over the past century, threatening the safety and standards of living of the world’s 800 million inhabitants of coastal cities. Despite renewed commitments to fight the causes of climate change during the COP26 climate negotiations in Glasgow, the current policies of the world’s largest polluting countries still put humanity on a dangerous path toward high levels of global warming and SLR for the decades and centuries to come. Based on the latest scientific publications, including the IPCC’s Assessment Report 6, this chapter sheds light on how this phenomenon is expected to affect in a multi-dimensional manner the safety and standards of living of coastal city inhabitants across the Arab region, and especially in the Arabian Gulf sub-region, in the decades and centuries to come. Studying the case of Doha, we highlight several policy challenges and opportunities that could influence the hazards as well as the levels of vulnerability and exposure to which individual Arab coastal cities are exposed to. The authors conclude that collectively fighting the causes of climate change, better planning urban and coastal development, as well as innovating for the climate adaptation of Arab coastal cities should be understood by policymakers, the private sector, and populations alike as a national security challenge that requires urgent individual and collective action.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".